store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 40, 'num_iterations': 20, 'no_cuda': False, 'name': 'bald_40_74940', 'seed': 74940, 'log_interval': 20, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[53381, 49220, 35430, 18541, 53138, 18850, 53903, 28174, 6215, 56094, 15918, 24910, 30024, 48219, 44787, 57235, 13342, 49290, 33147, 33855]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6519, 'nll': 2.359406339263916}, 'chosen_samples': [29714, 40234, 31472, 18428, 30064, 6077, 5867, 36748, 34558, 14329, 34973, 23001, 40437, 40786, 28353, 32159, 55861, 38417, 991, 43735, 44364, 32521, 55245, 35232, 21880, 958, 43161, 26034, 4646, 12679, 43991, 22335, 24902, 41133, 49525, 57125, 1309, 9862, 37803, 46978], 'chosen_samples_score': ['0.9883566', '0.9886562', '0.9931349', '0.9936703', '0.9943078', '0.9968439', '1.0073247', '1.001255', '1.0065103', '1.0023799', '0.99781835', '0.99989724', '1.0103835', '1.0044291', '1.0005887', '0.9979836', '1.0136011', '1.0153799', '1.0793895', '1.155978', '1.0201705', '1.0301592', '1.0346844', '1.0602443', '1.0569885', '1.0203662', '1.0174339', '1.0163056', '1.0630088', '1.0192137', '1.0348314', '1.1018896', '1.0408282', '1.0216079', '1.154831', '1.0239955', '1.1904488', '1.0560901', '1.0533333', '1.068053']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6993, 'nll': 1.4961291446685792}, 'chosen_samples': [6973, 28444, 14039, 17335, 42632, 43026, 56200, 14635, 19689, 21650, 24671, 27337, 44030, 1772, 29760, 31446, 31851, 5728, 3882, 55953, 28419, 20429, 10210, 29530, 48540, 40057, 44180, 26901, 59294, 11654, 10107, 36984, 15519, 22983, 57632, 42797, 22802, 24343, 52747, 32760], 'chosen_samples_score': ['0.87594396', '0.87641907', '0.8774525', '0.8775151', '0.87832373', '0.87871945', '0.92333126', '0.87998736', '0.92536294', '0.9547487', '0.8848801', '0.94123584', '0.9128942', '0.8910701', '0.91538', '0.9003491', '0.8810574', '0.9936816', '0.88844436', '0.9068375', '0.8825099', '0.9014404', '0.9702688', '0.9208067', '0.9175299', '0.9328115', '0.8886505', '0.8901912', '0.926704', '0.87999076', '0.91602176', '0.88611597', '0.89815605', '0.9041325', '0.89153296', '0.88356876', '0.92851424', '0.88970536', '0.90446556', '0.98120344']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7811, 'nll': 1.0503324264526368}, 'chosen_samples': [38651, 31924, 44271, 43729, 36343, 31806, 19855, 11708, 22561, 10481, 18985, 16822, 13831, 25499, 36724, 2061, 18397, 20057, 52197, 40526, 1239, 45057, 39526, 45516, 19959, 35954, 46379, 9379, 25332, 6213, 24219, 37044, 24017, 48507, 3432, 55826, 6348, 49472, 23388, 42504], 'chosen_samples_score': ['0.6877657', '0.6883137', '0.6891964', '0.68964124', '0.68965673', '0.6897449', '0.6919138', '0.6905623', '0.69211495', '0.7010045', '0.6935048', '0.75346625', '0.70017785', '0.73001647', '0.6967384', '0.7508833', '0.696617', '0.7487749', '0.7398181', '0.73952234', '0.765753', '0.6969791', '0.7230777', '0.7670087', '0.81357116', '0.7653007', '0.7069187', '0.70725447', '0.69779354', '0.71599156', '0.7010538', '0.723649', '0.8096549', '0.69857144', '0.71037316', '0.73719716', '0.7375453', '0.7209742', '0.6942883', '0.74021304']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8577, 'nll': 0.7386561809539794}, 'chosen_samples': [31430, 20172, 14249, 49564, 12589, 879, 31014, 17603, 56213, 20569, 42577, 31900, 6149, 8676, 38772, 27062, 18586, 17159, 7033, 55591, 54666, 9081, 20183, 37313, 7052, 8339, 47028, 32387, 30370, 49002, 51415, 10044, 50562, 20050, 24632, 5303, 37050, 27477, 3447, 10904], 'chosen_samples_score': ['0.80896366', '0.8107616', '0.81208044', '0.81181794', '0.8123883', '0.8167347', '0.81578934', '0.81244415', '0.8173805', '0.8797571', '0.875451', '0.8215254', '0.826961', '0.9495982', '0.82802886', '0.8625999', '0.81940657', '0.8811876', '0.9670003', '0.87153643', '0.8785904', '0.8272919', '0.87250596', '0.8592025', '0.8466463', '0.85424244', '0.83969504', '0.81790155', '0.8602073', '0.8231258', '1.0746686', '0.86773413', '0.8527211', '0.89037305', '0.8661988', '0.90633154', '0.840879', '0.82150203', '0.82881624', '0.8800682']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9136, 'nll': 0.5725983695030212}, 'chosen_samples': [6466, 19344, 51736, 38898, 37293, 52169, 27172, 22217, 26622, 26412, 57211, 27838, 23863, 55531, 59747, 30692, 134, 2765, 21383, 3765, 31184, 14767, 15406, 31293, 49590, 54043, 23629, 32323, 2803, 42515, 49607, 7768, 2636, 56300, 49545, 28420, 9390, 41453, 15191, 41572], 'chosen_samples_score': ['0.9531135', '0.9594575', '0.9558379', '0.9610365', '0.9572835', '0.9647745', '0.9566165', '0.9594001', '0.9653672', '0.9614451', '0.96552145', '0.95582783', '0.97096777', '0.9907205', '1.1329229', '1.0288146', '1.092857', '1.0226395', '0.994864', '1.0464096', '1.0179994', '1.0792375', '0.98571396', '1.0404733', '0.99178344', '1.0116742', '1.1033809', '1.1420022', '1.055331', '1.1684568', '1.037008', '1.0015929', '1.0120244', '1.1000674', '1.1174299', '1.0270545', '0.99594855', '0.97648513', '1.030703', '0.982229']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9398, 'nll': 0.4641681192398071}, 'chosen_samples': [24424, 52095, 31456, 3461, 12840, 32880, 8765, 13942, 13878, 43560, 37926, 42503, 31308, 28371, 8093, 8842, 17697, 33426, 35946, 3762, 588, 43042, 12297, 30326, 3494, 5684, 27473, 32002, 5600, 12768, 3289, 48997, 18398, 15432, 50916, 27335, 28536, 45982, 25508, 23140], 'chosen_samples_score': ['0.9309774', '0.93368965', '0.9360925', '0.9380134', '0.93811506', '0.9387642', '0.93816715', '0.93912756', '0.9600461', '0.9524683', '0.95245016', '0.9474809', '0.9495938', '0.96059144', '0.93932664', '0.961595', '0.9415053', '0.95644647', '0.95026046', '0.96165526', '0.98208153', '1.0438371', '1.1086526', '0.9859473', '1.0735352', '0.99503136', '1.0515015', '0.99616855', '0.9776407', '0.97901964', '1.002167', '0.9621848', '1.0261033', '0.9703727', '0.99836355', '1.0066442', '0.98265386', '0.97309166', '0.98415005', '1.0033453']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9496, 'nll': 0.41241757078170777}, 'chosen_samples': [48973, 59468, 11378, 16210, 16756, 8714, 18728, 41613, 33594, 29938, 57240, 19814, 53114, 39834, 42437, 56014, 37538, 670, 25246, 3146, 44361, 866, 19820, 25359, 41772, 43961, 5315, 54798, 27429, 27458, 1088, 9559, 36760, 3968, 28305, 34771, 12786, 38698, 37691, 31562], 'chosen_samples_score': ['0.9268814', '0.93402857', '0.9306377', '0.9275297', '0.9343969', '0.928128', '0.92940056', '0.9349878', '0.9354742', '0.94025916', '0.93864423', '0.9418471', '0.9419604', '0.94301903', '0.98395723', '0.9447634', '1.0472394', '0.952523', '0.9799601', '0.965829', '1.0022613', '0.94681746', '0.94959456', '1.0852734', '1.0457705', '1.0336709', '1.0120187', '1.1095018', '1.0174099', '0.96736354', '1.0318897', '0.94326586', '0.95447963', '0.96413845', '0.9533641', '0.9581505', '1.0051732', '1.1261097', '0.9823529', '0.97155344']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9599, 'nll': 0.3579738457202911}, 'chosen_samples': [42844, 18740, 38275, 50572, 5013, 54950, 42784, 137, 17055, 35688, 10187, 52319, 18487, 1328, 51614, 37078, 37672, 46412, 22083, 4165, 32047, 31252, 11946, 2064, 788, 26588, 12581, 4822, 49890, 10800, 16456, 29672, 26358, 29286, 9384, 45944, 15325, 23086, 5103, 40732], 'chosen_samples_score': ['0.9410812', '0.94258153', '0.9427878', '0.9503659', '0.9513635', '0.9531459', '0.96660006', '0.95689976', '0.96282136', '0.9570767', '0.97298443', '0.96757656', '0.95385015', '0.9748674', '1.1877778', '1.1105132', '0.98716563', '1.0731727', '1.0044649', '1.0969173', '1.046546', '0.98466057', '0.9789831', '0.9788281', '0.9936017', '0.9950369', '1.0371805', '1.0662153', '1.0327697', '1.0380018', '1.0755057', '1.0499911', '0.99411577', '0.9784903', '1.0054755', '1.0577801', '0.98756427', '1.0185765', '0.99686867', '1.0546212']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.954, 'nll': 0.3696702157974243}, 'chosen_samples': [58470, 26733, 56719, 3392, 14697, 42703, 52097, 8445, 24633, 45026, 32364, 24038, 42715, 14355, 24462, 37469, 626, 57728, 28930, 3030, 22272, 41371, 274, 57523, 52851, 18224, 44245, 49499, 7879, 55268, 14367, 15743, 37347, 59314, 3691, 21668, 30089, 45069, 49099, 17043], 'chosen_samples_score': ['0.8305349', '0.83110875', '0.8317139', '0.8329896', '0.8467156', '0.83811086', '0.8349208', '0.8362694', '0.8405628', '0.8360747', '0.84561855', '0.83966506', '0.849367', '0.86245686', '0.88639826', '0.97350985', '0.8760866', '0.8877971', '1.0212305', '0.87374', '0.85495406', '0.93192124', '0.8746528', '0.86332244', '0.8928806', '0.96039015', '0.8500173', '0.85397476', '0.86881906', '1.0129597', '0.8855319', '0.9483085', '0.90308845', '0.8840285', '0.92390436', '0.88069284', '0.8696737', '0.8882299', '0.8517628', '0.87708503']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9638, 'nll': 0.3262585597991943}, 'chosen_samples': [29388, 5124, 7440, 38389, 46715, 42573, 39363, 10982, 13714, 52225, 22139, 46623, 58050, 41108, 17010, 22497, 414, 5170, 21023, 54994, 36450, 49692, 16836, 39841, 2761, 39877, 886, 21174, 24479, 20169, 3470, 50522, 1674, 7793, 5536, 45800, 6808, 50317, 46086, 39054], 'chosen_samples_score': ['0.9049696', '0.90538263', '0.90600365', '0.90625304', '0.9104767', '0.91074103', '0.9170932', '0.9128517', '0.91418445', '0.91083235', '0.9108643', '0.9091274', '0.9115486', '0.9142038', '0.91999006', '0.9375742', '0.93324083', '0.97314173', '0.94355935', '0.9491439', '0.9287159', '0.985763', '0.95020556', '0.94334984', '0.93478656', '0.93365866', '0.9219543', '1.0295761', '1.0399168', '1.0177386', '0.9681238', '0.9606288', '0.9344949', '0.9734359', '0.9337604', '0.92014', '0.9520193', '1.0311391', '0.92426634', '0.9407606']})
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store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9656, 'nll': 0.3284886154174805}, 'chosen_samples': [12663, 32979, 36417, 24567, 41454, 55702, 44582, 1376, 49284, 46284, 18568, 31046, 58466, 6186, 46780, 11634, 52163, 33654, 9290, 39405, 42384, 24080, 34186, 32016, 35654, 12305, 52668, 3676, 8297, 23473, 52671, 20746, 37116, 22952, 18704, 36822, 44753, 4160, 51993, 45864], 'chosen_samples_score': ['0.7903031', '0.7915053', '0.79058623', '0.79305536', '0.7920609', '0.7970539', '0.7995458', '0.81076694', '0.8059303', '0.8105287', '0.808643', '0.7992757', '0.807896', '0.8038873', '0.80900633', '0.797662', '0.80328757', '0.81582797', '0.8261262', '0.82568604', '0.8293027', '0.8177223', '0.8341476', '0.8290189', '0.8542582', '0.8242919', '0.86054975', '0.90045375', '0.86338866', '0.9421013', '0.8780328', '0.8776744', '0.8894556', '0.8983812', '0.9466732', '0.8841691', '0.87163', '0.8939974', '0.89165914', '0.8631355']})
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